Skip to search formSkip to main contentSkip to account menu

A Benchmark and Simulator for UAV Tracking

A new aerial video dataset and benchmark for low altitude UAV target tracking, as well as, a photo-realistic UAV simulator that can be coupled with tracking methods to easily extend existing real-world datasets.
View via Publisher (opens in a new tab)

ActivityNet: A large-scale video benchmark for human activity understanding

This paper introduces ActivityNet, a new large-scale video benchmark for human activity understanding that aims at covering a wide range of complex human activities that are of interest to people in their daily living.
View on IEEE (opens in a new tab)

The Visual Object Tracking VOT2016 Challenge Results

The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
View via Publisher (opens in a new tab)

TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild

This work presents TrackingNet, the first large-scale dataset and benchmark for object tracking in the wild, which covers a wide selection of object classes in broad and diverse context and provides an extensive benchmark on TrackingNet by evaluating more than 20 trackers.
View PDF on arXiv (opens in a new tab)

Robust visual tracking via multi-task sparse learning

Experimental results show that MTT methods consistently outperform state-of-the-art trackers and mining the interdependencies between particles improves tracking performance and overall computational complexity.
View on IEEE (opens in a new tab)

ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing

This paper proposes a novel structured deep network, dubbed ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a general $$ norm CS reconstruction model and develops an effective strategy to solve the proximal mapping associated with the sparsity-inducing regularizer using nonlinear transforms.
View on IEEE (opens in a new tab)

Context-Aware Correlation Filter Tracking

This paper reformulate the original optimization problem and provides a closed form solution for single and multi-dimensional features in the primal and dual domain and significantly improves the performance of many CF trackers with only a modest impact on frame rate.
View on IEEE (opens in a new tab)

DeepGCNs: Can GCNs Go As Deep As CNNs?

This work presents new ways to successfully train very deep GCNs by borrowing concepts from CNNs, specifically residual/dense connections and dilated convolutions, and adapting them to GCN architectures, and building a very deep 56-layer GCN.
View on IEEE (opens in a new tab)

G-TAD: Sub-Graph Localization for Temporal Action Detection

This work proposes a graph convolutional network (GCN) model to adaptively incorporate multi-level semantic context into video features and cast temporal action detection as a sub-graph localization problem.
View on IEEE (opens in a new tab)

The Visual Object Tracking VOT2015 Challenge Results

The Visual Object Tracking challenge 2015, VOT 2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and presents a new VOT2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute.
...
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)